{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 阶段五模块五作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 高中体测数据可视化（体测分数_男生，体测分数-女生）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0        0  2785      62  170  72.599998  25.120001  \n",
       "1       60  3133      68  174  52.700001  17.410000  \n",
       "2        0  3901      80  169  46.500000  16.280001  \n",
       "3        0  4946     100  183  79.699997  23.799999  \n",
       "4       68  3538      74  171  54.700001  18.709999  \n",
       "..     ...   ...     ...  ...        ...        ...  \n",
       "472      0  4647     100  176  69.500000  22.440001  \n",
       "473     50  7042     100  177  76.000000  24.260000  \n",
       "474     85  5755     100  181  65.000000  19.840000  \n",
       "475     76  5688     100  172  51.700001  17.480000  \n",
       "476      0     0       0    0   0.000000   0.000000  \n",
       "\n",
       "[477 rows x 17 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boy = pd.read_excel(r'阶段五模块五作业数据/体测分数_男生.xls')\n",
    "df_boy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          (3.647, 5.47]\n",
       "1          (3.647, 5.47]\n",
       "2          (3.647, 5.47]\n",
       "3          (3.647, 5.47]\n",
       "4         (1.823, 3.647]\n",
       "             ...        \n",
       "472        (3.647, 5.47]\n",
       "473        (3.647, 5.47]\n",
       "474       (1.823, 3.647]\n",
       "475        (3.647, 5.47]\n",
       "476    (-0.00547, 1.823]\n",
       "Name: 男1000米跑, Length: 477, dtype: category\n",
       "Categories (3, interval[float64]): [(-0.00547, 1.823] < (1.823, 3.647] < (3.647, 5.47]]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_1000 = pd.cut(df_boy['男1000米跑'],bins=3)  #对男1000米跑进行等宽分箱操作,分成3份\n",
    "data_1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 7], (0, 7], (0, 7], (0, 7], (7, 15], ..., NaN, (0.0, 7.0], (7.0, 15.0], (7.0, 15.0], NaN]\n",
       "Length: 477\n",
       "Categories (3, interval[int64]): [(0, 7] < (7, 15] < (15, 23]]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boy['男引体'] = df_boy['男引体'].apply(lambda x : int(x))\n",
    "df_boy['男引体']\n",
    "data_yt = pd.cut(df_boy['男引体'].values,bins=[0,7,15,23])  #对男引体进行等宽分箱操作,分成3份\n",
    "data_yt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2ca0a261b80>,\n",
       "  <matplotlib.patches.Wedge at 0x2ca0a2094f0>,\n",
       "  <matplotlib.patches.Wedge at 0x2ca0a209dc0>],\n",
       " [Text(-0.17717585164356228, 1.1868482285424613, '(3.647, 5.47]'),\n",
       "  Text(0.010866959209503178, -1.0999463210527771, '(1.823, 3.647]'),\n",
       "  Text(1.0894955895965468, -0.15165539967199632, '(-0.00547, 1.823]')],\n",
       " [Text(-0.10335258012541133, 0.6923281333164357, '54.7%'),\n",
       "  Text(0.005927432296092642, -0.5999707205742421, '40.9%'),\n",
       "  Text(0.5942703215981164, -0.08272112709381615, '4.4%')])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='SimHei'\n",
    "labels = data_1000.value_counts().index.to_list()\n",
    "percent = data_1000.value_counts().values.tolist()\n",
    "fig = plt.figure(figsize=(4,4),dpi=150)\n",
    "plt.title('男生1000米跑体测成绩')\n",
    "plt.pie(x = percent,\n",
    "        labels=labels,\n",
    "        explode = (0.1,0,0),\n",
    "        autopct='%0.1f%%',\n",
    "        shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2ca79f4c8e0>,\n",
       "  <matplotlib.patches.Wedge at 0x2ca79f4ce50>,\n",
       "  <matplotlib.patches.Wedge at 0x2ca08026280>],\n",
       " [Text(0.2455993299178982, 1.174598216048313, '(0, 7]'),\n",
       "  Text(-0.3754055962126714, -1.0339587217738477, '(7, 15]'),\n",
       "  Text(1.088904543865263, -0.15584253061851952, '(15, 23]')],\n",
       " [Text(0.1432662757854406, 0.6851822926948492, '43.4%'),\n",
       "  Text(-0.20476668884327529, -0.5639774846039168, '52.0%'),\n",
       "  Text(0.593947933017416, -0.08500501670101064, '4.5%')])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='SimHei'\n",
    "labels = data_yt.value_counts().astype('int').index.tolist()\n",
    "percent = data_yt.value_counts().values.tolist()\n",
    "fig = plt.figure(figsize=(4,4),dpi=150)\n",
    "plt.title('男生引体体测成绩')\n",
    "plt.pie(x = percent,\n",
    "        labels=labels,\n",
    "        explode = (0.1,0,0),\n",
    "        autopct='%0.1f%%',\n",
    "        shadow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0       85  3775     100  163.0  51.299999  19.309999  \n",
       "1       66  3683     100  163.0  66.599998  25.070000  \n",
       "2       76  3331     100  157.0  60.000000  24.340000  \n",
       "3       85  3701     100  160.0  50.700001  19.799999  \n",
       "4       70  3592     100  167.0  63.900002  22.910000  \n",
       "..     ...   ...     ...    ...        ...        ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999  \n",
       "589     72  2937      85  161.0  55.700001  21.490000  \n",
       "590     72  2592      76  165.0  48.599998  17.850000  \n",
       "591     78  1829      60  154.0  43.599998  18.379999  \n",
       "592     85  2962      85  162.0  55.299999  21.070000  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_girl = pd.read_excel(r'阶段五模块五作业数据/体测分数_女生.xls')\n",
    "df_girl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVwklEQVR4nO3df5RfdX3n8eeLBGhIULGMsdmCiLJVV43KrAUOaHBBRXRdUrukYnu62KZure3Zbj3ikq3VjdSyLrX150mLLmurlGpFQfxBpWxjETRRcBXUdtsgP2SNHMo06grF9/5xbzZfhu8kdzJT8pmZ5+OcOdx5533v93PvmcyLz/1+vjepKiRJas1BB3oAkiSNY0BJkppkQEmSmmRASZKaZEBJkppkQEkiyUSS5UkOPtBjkXYzoLRkJXlRkquSZC89a5Mc1G//dpL/8jCNbdXI9g1JXtBvPzLJYwbs/9okp83iJT8CnAT8aZJzph3rXyZ565jX+HaSQ2fxGtKsGFBaytYBn6+9fxjwSuAl/fYP+q+xkvxmktuT3JFk40j9VUnuSrItyeNH6m/qf8l/JsmjR+pPAG5Ocnhf+s7I6/4KXZiMvu7O/tjfSPLWPnA3Ajfv4/xHfR/4R+CVwC+Ojgc4E/he/1oXJPlkkk8CjwA+2n//oVm8ljTI8gM9AOkAWg+8YKY/TLIW+CHwsX0dKMlTgecCjwPWANuTfBR4DPCfgWf1f/YO4MwkZwJnAU+kC4DNwC8DVNX/TvJF4HzgPOD+/jV+FPg14EXTXv62qppM8kLgOcDz6MLjY/3kcAXw9apa3x/n9cDZdIG02xOA9wK7gB8B3gn8TH/L72zguf3rvxs4tN/3c8CrgGX9lzSvDCgtGUl+HLiabjawHDgK+JORO3yrgO/S/QI+E/gl4DDglr7nSKCSvAIIcDhwVlXdADwZ2FZVDwC3JdkBPBb418D/qKo7gTuTHJlkJV04vqOqppJcCvynacN9A/DmabWTgD+tqm3T6gdN2349cEZVfbE/72cA/3GkZwJ4W1X995FrcxHwV1X14STL+vMA+A/Atv5c/wJ4MXARXUAdAbyHLpxeizTPDCgtGVV1O12QkGQzcFlVvbn//ieAzwBPrqpKcizwiv77O/qe3wL+sao2jzn8zcCbkryNblY0AdxCNyu6dqTvTrqZ1I8Df9iPq5Lc3wfXfcADVXUTXRiMjv8K4Ir+Ft4hwH397cn/k+SbwD8AN9AFxqeTfA1YSXe77/7RQ40Z/53AY/r32z6XZENV/S1wY3+8q4Hf6Mf0yX6f/wt8tt9+UZLvVdU3xhxb2i8GlJackdtWJ46Unw/82cj7UY+kC5dv7eU4B1XVD/tvbwZuAz5ON9t4a1Xdl2QZMDWy23eBR9H90h+tf69/zc3As5Psvv12HN0tupcmuZkuaJb1r3Fykm8BLwdeQzeDuzLJE4G3ABvoAuQgHhxQAS5Ich5dkG4C7gAmgVPpbg/eBlBVn05yPPDHdOF3LfBnI8f6N/1/X2k4ab4ZUFqKXkp36+6UJB+jm1G8CviZ3Q1V9SXgS0nekuRldLe0Rm/xHUI3A7qg3+Xngb+rquf3s5BPJfk8cA9dIO22gu59rbH1qjp3d6EPmk8BX6cLs7ur6tWjJ5LkKOBSuhnOy/v3wi6ddr4H8+CAOgz49aq6tJ/x7exf4xeBpwNvqKrd73udBbwR+DKwFvgFYFVVPSPJvwWuAD7B+FmZNCeu4tOSU1Ufolug8BK6X8zvB75aVV8e03teVT2xqp5Et8Dh96rqSVV1bFVdMNJ6Av2quX5WdSPdjGQb/UytvzX3LOD2afXDgMcDd+8+WL+C71Lgt+hu+70HmEjyjiQrRl73fvaEz1foFi5At1DierqZ0Hl0t/92+zH2zN4eQbdK8Kt0tz8PAy4b6b0SWFlVk8DL6IKZJIcAF1XV9/s+F0lo3hlQWpKq6vP9bOVdwL8CTkzyO0km9vOQfwec3X9uah3dbOwrdLOLs5Ksp1vNd3f/XtiHgF9L8nzgvwHXjMxaTqW7nfaZqnr/yGucQxcQ30jyhpFl6Lt9mS54Du2PN1lVPw28jQcvuDge2NEvJX8y3QzqKXTvV326f0/sp/og/Dm6peQ3Al+gm/29ke4W6dVJXk4X3Hfu53WTZmRAaUlJclCSJ/WfTboBOIXuF/ZT6WYqX01yfjqHJnv9EG9GPqj6Trr3cf6Sbln6+6pqa1VN0c08fp1uGfo50C0lp3uP6wK6Zem/0h/zDOAPgE1V9brR16uq+6tqI134TdAtCX883WrE5wP/le4W3HJG/m5X1Z8DH0jy2P79pAfoZnE3At8GjgYupwudn0/yHLrQOarvuZBuhvgsupWJJ9KtOtxMd2vyQva8FyXNm/gPFmopSfJqusDYCnywqm6Z9ufH0gXJFXQLAn7AzO+vhG628qyqum8exzi6zJskV9EtC//0mN4L6RZefLSqbuxrB/dj3z3uH6G7BXc8Xfj9r6p6W9/7WrpFFi+vqluSnA58GNhZVU+Y9lpPBX6fbpHEe6tq94d3j6X7zNavVtV35+MaSGBASUtKv4AjuwMwyaOA71fVD0Z6HgEcXFV3jz+K9PAwoCRJTfI9KElSkwwoSVKTFtwHdY888sg65phjDvQwJEnzZPv27d+pqod8xGPBBdQxxxzDtm3Tn5UpSVqoktw6ru4tPklSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMW3JMkJGl/HXPexw/0EBadHW8585/s2M6gJElNMqAkSU0yoCRJTRoUUEkuTnJdkk2z6ZmhtjrJ1pHvj05ybZJrkmxJkv09GUnS4rHPgEqyHlhWVScBa5IcN6RnhtoRwCXAypHdfwn491X1POAo4GlzPy1J0kI3ZAa1Dris374GOHlgz7jaA8DZwNTuHavq/Kq6pf/2R4HvTD94ko1JtiXZtnPnzgFDliQtdEMCaiVwR789Bawe2POQWlVNVdW9414kydnAV6vqzul/VlVbqmqyqiYnJh7yjy5KkhahIZ+D2gWs6LdXMT7UxvUM2Q+AJMcCvwGcNmA8kqQlYMgMajt7buutBXYM7BmyH/37Uh8Ezp1pdiVJWnqGzKAuB7YmWQOcAWxIsrmqNu2l5wSgxtTGOQ84Gnh7v4DvDVX1P/fjXCRJi8g+A6qqppKsA04HLqyqu4Cb9tFzL8C4Wt+/bmT7dcDr5nQWkqRFZ9Cz+KrqHvasyBvcM2Q/SZLG8UkSkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJg0KqCQXJ7kuyabZ9MxQW51k68j3Bye5su87d39PRJK0uOwzoJKsB5ZV1UnAmiTHDemZoXYEcAmwcmT31wDb+r4XJzl8Hs5LkrTADZlBrQMu67evAU4e2DOu9gBwNjA1w77XAZMDxiRJWuSGBNRK4I5+ewpYPbDnIbWqmqqqe2d7/CQbk2xLsm3nzp0DhixJWuiGBNQuYEW/vWqGfcb1DNlv0PGraktVTVbV5MTExIAhS5IWuiEBtZ09t/XWAjsG9gzZb+jxJUlLzPIBPZcDW5OsAc4ANiTZXFWb9tJzAlBjauNcAlyV5BTgKcAN+3MikqTFZZ8zqKqaolvIcD1walXdNC2cxvXcO6420r9uZPtW4HTgr4DTquqBuZ2SJGkxGDKDoqruYc9Ku8E9Q/br++4c0idJWjp8koQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUmDAirJxUmuS7JpNj1DakmOSHJVkq1J3jOXk5EkLR77DKgk64FlVXUSsCbJcUN6htaAnwX+qKpOAQ5PMjmP5ydJWqCGzKDWAZf129cAJw/sGVq7G/iJJI8CjgK+Of3gSTYm2ZZk286dOwcMWZK00A0JqJXAHf32FLB6YM/Q2meB44BfBb4G3DP94FW1paomq2pyYmJiwJAlSQvdkIDaBazot1fNsM+4nqG1C4BXVdWb6ALq383uFCRJi9GQgNrOntt6a4EdA3uG1g4DnpZkGfCTQA0fviRpsVo+oOdyYGuSNcAZwIYkm6tq0156TqALmiG1vwHeBzwO+BzwwXk4L0nSArfPGVRVTdEtbrgeOLWqbpoWTuN67p1F7fNV9S+qalVVnV5Vu+bv9CRJC9WQGRRVdQ97Vt8N7hlakyRpOp8kIUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJatKggEpycZLrkmyaTc/QWl9/V5KX7M9JSJIWn30GVJL1wLKqOglYk+S4IT1Da/3+pwCPraor5vHcJEkL2JAZ1Drgsn77GuDkgT2DakkOBv4A2JHkpeMGkGRjkm1Jtu3cuXPAkCVJC92QgFoJ3NFvTwGrB/YMrf0ccDNwIfDsJK+ZfvCq2lJVk1U1OTExMWDIkqSFbkhA7QJW9NurZthnXM/Q2jOBLVV1F/BHwKmzOwVJ0mI0JKC2s+e23lpgx8CeobW/AY7ta5PArcOGLklazJYP6Lkc2JpkDXAGsCHJ5qratJeeE4AaWPsh8N4kG4CDgZfNx4lJkha2fQZUVU0lWQecDlzY34q7aR899wIMrQE/PdcTkSQtLkNmUFTVPexZfTe4Z2hNkqTpfJKEJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUmDnsUn6eF3zHkfP9BDkA4oZ1CSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYNCqgkFye5Lsmm2fQMrfX11Um+tD8nIUlafPYZUEnWA8uq6iRgTZLjhvQMrY0c5q3Aivk4KUnSwjdkBrUOuKzfvgY4eWDP0BpJngd8F7hr3ACSbEyyLcm2nTt3DhiyJGmhGxJQK4E7+u0pYPXAnkG1JIcAvwmcN9MAqmpLVU1W1eTExMSAIUuSFrohAbWLPbfeVs2wz7ieobXzgHdW1d/PcuySpEVsSEBtZ89tvbXAjoE9Q2unAa9Oci3wjCR/OHz4kqTFavmAnsuBrUnWAGcAG5JsrqpNe+k5Aaghtar6wO6DJLm2qn5h7qclSVro9jmDqqopusUN1wOnVtVN08JpXM+9Q2vTjrNujucjSVokhsygqKp72LP6bnDP0JokSdP5JAlJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTBgVUkouTXJdk02x6htSSPDLJJ5JcneQjSQ6ZywlJkhaHfQZUkvXAsqo6CViT5LghPUNrwDnARVV1OnAX8ML5Oz1J0kK1fEDPOuCyfvsa4GTgrwf0PHNIrareNXKcCeDb0weQZCOwEeDoo48eMGRJ0kI35BbfSuCOfnsKWD2wZ2gNgCQnAkdU1fXTD15VW6pqsqomJyYmBgxZkrTQDZlB7QJW9NurGB9q43qG1kjyaODtwE/NbviSpMVqyAxqO93tOYC1wI6BPYNq/aKIy4DXV9Wtsxq9JGnRGjKDuhzYmmQNcAawIcnmqtq0l54TgBpYeyVwPHB+kvOBd1fVn8zHyUmSFq59BlRVTSVZB5wOXFhVdwE37aPnXoCBtXf3X5Ik/X9DZlBU1T3sWX03uGdoTZKk6XyShCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJgz6ou9gcc97HD/QQFp0dbznzQA9B0iLjDEqS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktSkJfmwWM0/H8Arab45g5IkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDVpUEAluTjJdUk2zaZnLjVJ0tK2z4BKsh5YVlUnAWuSHDekZy61+TxBSdLCNOSDuuuAy/rta4CTgb8e0PPMOdQedPwkG4GN/be7knx92usfCXxnwLloZl7DufMazp3XcO4e1muY35mXwzxuXHFIQK0E7ui3p4AnDuyZS+1BqmoLsGWmASbZVlWTA85FM/Aazp3XcO68hnO3mK7hkPegdgEr+u1VM+wzrmcuNUnSEjckDLbT3XYDWAvsGNgzl5okaYkbcovvcmBrkjXAGcCGJJuratNeek4Aag612Zrx9p8G8xrOnddw7ryGc7dormGqat9NyRHA6cBfVtVdQ3vmUpMkLW2DAkqSpIebCxIkSU1a8AHlUyiGS7I8yTeTXNt/PS3JG5N8Ick7RvoeUhMkWZ1ka799cJIr+5+9c2dTW8qmXcN/luT2kZ/Hib7u02bGSPLIJJ9IcnWSjyQ5ZLE/rWdBB5RPoZi1pwMfrKp1VbUOOJRuBeWzgduTnJZkcnrtgI22If37pJfQfW4P4DXAtv5n78VJDp9FbUkacw1/Enjz7p/Hqtrp02b26hzgoqo6HbgL2MAif1rPgg4oxj/BQjM7ATgryWeT/DHwPODD1b0R+efAKcBzxtQEDwBn032YHB78s3cdMDmL2lI1/RqeAPxyks8l+d2+to6H/p0eV1tyqupdVXV1/+0E8AqGXatxtQVhoQfU9KdQrD6AY1kIvgA8t6pOBv6e7gPS06+f13SMqpqqqntHSuOu09DakjTmGn4COKmqTgT+eZKn4zXcpyQnAkcAt7HIfwYXekD5FIrZ+XJVfavf/ho+2WMufFLK3F1XVf/Qb38NOA6v4V4leTTwduBclsDP4IIZ6Ax8CsXsvD/J2iTLgLPo/s/KJ3vsH5+UMnefSvJjSQ4DXgB8Ba/hjJIcQner7vVVdStL4GdwyJMkWnY5c38KxVLyJuADQICPAZvprt/vAS/sv24FfntaTQ91CXBVklOApwA30N1GGVJT543AXwD3Ae+pqq8n+Rb/NE+bWQxeCRwPnJ/kfOB9wM8+TE/rOSAW/Ad1fQrF3CRZAZwJfLGq/nammh6q/wt/MvCp3e+tDK1pZj5tZrjF/rSeBR9QkqTFaaG/ByVJWqQMKElSkwwoSVKTDChJUpMMKElSk/4f/WfhAm+WPm4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='SimHei'\n",
    "x = df_girl['女800米跑分数'].mean().round(0) + df_girl['女800米跑分数'].std().round(0) * df_girl['女800米跑分数']\n",
    "fig,ax = plt.subplots()\n",
    "plt.title('女生800米跑成绩')\n",
    "n, bins, patches = ax.hist(x, bins = 4, density=True)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['font.sans-serif']='SimHei'\n",
    "x = df_girl['女跳远分数'].mean().round(0) + df_girl['女跳远分数'].std().round(0) * df_girl['女跳远分数']\n",
    "fig,ax = plt.subplots()\n",
    "plt.title('女生跳远成绩')\n",
    "n, bins, patches = ax.hist(x, bins = 4, density=True)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖(男女生体重指数参考如下)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./boy_BMI.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./girl_BMI.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>正常</th>\n",
       "      <th>低体重</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  正常  低体重  \n",
       "0        0  2785      62  170  72.599998  25.120001   0    0  \n",
       "1       60  3133      68  174  52.700001  17.410000   1    0  \n",
       "2        0  3901      80  169  46.500000  16.280001   0    1  \n",
       "3        0  4946     100  183  79.699997  23.799999   0    0  \n",
       "4       68  3538      74  171  54.700001  18.709999   1    0  \n",
       "..     ...   ...     ...  ...        ...        ...  ..  ...  \n",
       "472      0  4647     100  176  69.500000  22.440001   1    0  \n",
       "473     50  7042     100  177  76.000000  24.260000   0    0  \n",
       "474     85  5755     100  181  65.000000  19.840000   1    0  \n",
       "475     76  5688     100  172  51.700001  17.480000   1    0  \n",
       "476      0     0       0    0   0.000000   0.000000   0    1  \n",
       "\n",
       "[477 rows x 19 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
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       "      <th>男50米跑</th>\n",
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       "      <th>男引体</th>\n",
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       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
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       "      <th>正常</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
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       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
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       "      <td>14</td>\n",
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       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>1</td>\n",
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       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
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       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
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       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
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       "      <td>15</td>\n",
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       "      <td>24.260000</td>\n",
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       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
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       "      <td>17.480000</td>\n",
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       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  正常  \n",
       "0        0  2785      62  170  72.599998  25.120001   0  \n",
       "1       60  3133      68  174  52.700001  17.410000   1  \n",
       "2        0  3901      80  169  46.500000  16.280001   0  \n",
       "3        0  4946     100  183  79.699997  23.799999   0  \n",
       "4       68  3538      74  171  54.700001  18.709999   1  \n",
       "..     ...   ...     ...  ...        ...        ...  ..  \n",
       "472      0  4647     100  176  69.500000  22.440001   1  \n",
       "473     50  7042     100  177  76.000000  24.260000   0  \n",
       "474     85  5755     100  181  65.000000  19.840000   1  \n",
       "475     76  5688     100  172  51.700001  17.480000   1  \n",
       "476      0     0       0    0   0.000000   0.000000   0  \n",
       "\n",
       "[477 rows x 18 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_1(x):\n",
    "    if 16.5<= x <=23.2:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df_boy['正常'] = df_boy['BMI'].apply(convert_1)\n",
    "df_boy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
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       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
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       "      <th>475</th>\n",
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       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
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      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  正常  低体重  \n",
       "0        0  2785      62  170  72.599998  25.120001   0    0  \n",
       "1       60  3133      68  174  52.700001  17.410000   1    0  \n",
       "2        0  3901      80  169  46.500000  16.280001   0    1  \n",
       "3        0  4946     100  183  79.699997  23.799999   0    0  \n",
       "4       68  3538      74  171  54.700001  18.709999   1    0  \n",
       "..     ...   ...     ...  ...        ...        ...  ..  ...  \n",
       "472      0  4647     100  176  69.500000  22.440001   1    0  \n",
       "473     50  7042     100  177  76.000000  24.260000   0    0  \n",
       "474     85  5755     100  181  65.000000  19.840000   1    0  \n",
       "475     76  5688     100  172  51.700001  17.480000   1    0  \n",
       "476      0     0       0    0   0.000000   0.000000   0    1  \n",
       "\n",
       "[477 rows x 19 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_2(x):\n",
    "    if x <=16.4:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df_boy['低体重'] = df_boy['BMI'].apply(convert_2)\n",
    "df_boy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "      <td>18.709999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.23</td>\n",
       "      <td>68</td>\n",
       "      <td>8.27</td>\n",
       "      <td>72</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4647</td>\n",
       "      <td>100</td>\n",
       "      <td>176</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>22.440001</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>5.19</td>\n",
       "      <td>40</td>\n",
       "      <td>9.55</td>\n",
       "      <td>50</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>7042</td>\n",
       "      <td>100</td>\n",
       "      <td>177</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.260000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>3.25</td>\n",
       "      <td>100</td>\n",
       "      <td>7.50</td>\n",
       "      <td>80</td>\n",
       "      <td>252</td>\n",
       "      <td>90</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>13</td>\n",
       "      <td>85</td>\n",
       "      <td>5755</td>\n",
       "      <td>100</td>\n",
       "      <td>181</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>19.840000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>4.39</td>\n",
       "      <td>62</td>\n",
       "      <td>7.81</td>\n",
       "      <td>76</td>\n",
       "      <td>208</td>\n",
       "      <td>66</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>5688</td>\n",
       "      <td>100</td>\n",
       "      <td>172</td>\n",
       "      <td>51.700001</td>\n",
       "      <td>17.480000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>17</td>\n",
       "      <td>男</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>477 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0     1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1     1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2     1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3     1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4     1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "..   .. ..      ...        ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "472  17  男     4.23         68   8.27       72  208     66    10      72    0   \n",
       "473  17  男     5.19         40   9.55       50  210     66    15      80    6   \n",
       "474  17  男     3.25        100   7.50       80  252     90    13      76   13   \n",
       "475  17  男     4.39         62   7.81       76  208     66    14      78   11   \n",
       "476  17  男     0.00          0   0.00        0    0      0     0       0    0   \n",
       "\n",
       "     男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  正常  低体重  超重  \n",
       "0        0  2785      62  170  72.599998  25.120001   0    0   1  \n",
       "1       60  3133      68  174  52.700001  17.410000   1    0   0  \n",
       "2        0  3901      80  169  46.500000  16.280001   0    1   0  \n",
       "3        0  4946     100  183  79.699997  23.799999   0    0   1  \n",
       "4       68  3538      74  171  54.700001  18.709999   1    0   0  \n",
       "..     ...   ...     ...  ...        ...        ...  ..  ...  ..  \n",
       "472      0  4647     100  176  69.500000  22.440001   1    0   0  \n",
       "473     50  7042     100  177  76.000000  24.260000   0    0   1  \n",
       "474     85  5755     100  181  65.000000  19.840000   1    0   0  \n",
       "475     76  5688     100  172  51.700001  17.480000   1    0   0  \n",
       "476      0     0       0    0   0.000000   0.000000   0    1   0  \n",
       "\n",
       "[477 rows x 20 columns]"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_3(x):\n",
    "    if 23.3<= x <=26.3:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df_boy['超重'] = df_boy['BMI'].apply(convert_3)\n",
    "df_boy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_4(x):\n",
    "    if x >=26.4:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df_boy['肥胖'] = df_boy['BMI'].apply(convert_4)\n",
    "df_boy['肥胖']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0       85  3775     100  163.0  51.299999  19.309999  \n",
       "1       66  3683     100  163.0  66.599998  25.070000  \n",
       "2       76  3331     100  157.0  60.000000  24.340000  \n",
       "3       85  3701     100  160.0  50.700001  19.799999  \n",
       "4       70  3592     100  167.0  63.900002  22.910000  \n",
       "..     ...   ...     ...    ...        ...        ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999  \n",
       "589     72  2937      85  161.0  55.700001  21.490000  \n",
       "590     72  2592      76  165.0  48.599998  17.850000  \n",
       "591     78  1829      60  154.0  43.599998  18.379999  \n",
       "592     85  2962      85  162.0  55.299999  21.070000  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_girl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>正常</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  正常  \n",
       "0       85  3775     100  163.0  51.299999  19.309999   1  \n",
       "1       66  3683     100  163.0  66.599998  25.070000   0  \n",
       "2       76  3331     100  157.0  60.000000  24.340000   0  \n",
       "3       85  3701     100  160.0  50.700001  19.799999   1  \n",
       "4       70  3592     100  167.0  63.900002  22.910000   0  \n",
       "..     ...   ...     ...    ...        ...        ...  ..  \n",
       "588     78  2255      70  158.0  49.000000  19.629999   1  \n",
       "589     72  2937      85  161.0  55.700001  21.490000   1  \n",
       "590     72  2592      76  165.0  48.599998  17.850000   1  \n",
       "591     78  1829      60  154.0  43.599998  18.379999   1  \n",
       "592     85  2962      85  162.0  55.299999  21.070000   1  \n",
       "\n",
       "[593 rows x 18 columns]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_1(x):\n",
    "    if 16.5<= x <=22.7:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df_girl['正常'] = df_girl['BMI'].apply(convert_1)\n",
    "df_girl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
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       "      <th>女50米跑</th>\n",
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       "      <th>女仰卧分数</th>\n",
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       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "      <th>正常</th>\n",
       "      <th>低体重</th>\n",
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       "      <td>66</td>\n",
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       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
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       "      <td>13.40</td>\n",
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       "      <td>150</td>\n",
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       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
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       "      <td>9.60</td>\n",
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       "      <td>60</td>\n",
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       "      <td>41</td>\n",
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       "      <td>70</td>\n",
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       "      <td>0</td>\n",
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       "      <th>589</th>\n",
       "      <td>17</td>\n",
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       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  正常  低体重  \n",
       "0       85  3775     100  163.0  51.299999  19.309999   1    0  \n",
       "1       66  3683     100  163.0  66.599998  25.070000   0    0  \n",
       "2       76  3331     100  157.0  60.000000  24.340000   0    0  \n",
       "3       85  3701     100  160.0  50.700001  19.799999   1    0  \n",
       "4       70  3592     100  167.0  63.900002  22.910000   0    0  \n",
       "..     ...   ...     ...    ...        ...        ...  ..  ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999   1    0  \n",
       "589     72  2937      85  161.0  55.700001  21.490000   1    0  \n",
       "590     72  2592      76  165.0  48.599998  17.850000   1    0  \n",
       "591     78  1829      60  154.0  43.599998  18.379999   1    0  \n",
       "592     85  2962      85  162.0  55.299999  21.070000   1    0  \n",
       "\n",
       "[593 rows x 19 columns]"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_2(x):\n",
    "    if x<=16.4:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df_girl['低体重'] = df_girl['BMI'].apply(convert_2)\n",
    "df_girl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
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       "      <td>9.32</td>\n",
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       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
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       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
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       "      <td>9</td>\n",
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       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
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       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
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       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
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       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>0</td>\n",
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       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
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       "      <td>70</td>\n",
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       "      <td>女</td>\n",
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       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
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       "      <td>21.490000</td>\n",
       "      <td>1</td>\n",
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       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
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       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
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       "      <td>1</td>\n",
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       "      <td>17</td>\n",
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       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  正常  低体重  超重  \n",
       "0       85  3775     100  163.0  51.299999  19.309999   1    0   0  \n",
       "1       66  3683     100  163.0  66.599998  25.070000   0    0   1  \n",
       "2       76  3331     100  157.0  60.000000  24.340000   0    0   1  \n",
       "3       85  3701     100  160.0  50.700001  19.799999   1    0   0  \n",
       "4       70  3592     100  167.0  63.900002  22.910000   0    0   1  \n",
       "..     ...   ...     ...    ...        ...        ...  ..  ...  ..  \n",
       "588     78  2255      70  158.0  49.000000  19.629999   1    0   0  \n",
       "589     72  2937      85  161.0  55.700001  21.490000   1    0   0  \n",
       "590     72  2592      76  165.0  48.599998  17.850000   1    0   0  \n",
       "591     78  1829      60  154.0  43.599998  18.379999   1    0   0  \n",
       "592     85  2962      85  162.0  55.299999  21.070000   1    0   0  \n",
       "\n",
       "[593 rows x 20 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_3(x):\n",
    "    if 22.8<=x<=25.2:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df_girl['超重'] = df_girl['BMI'].apply(convert_3)\n",
    "df_girl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>女肺活量分数</th>\n",
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       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  ...  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76  ...   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66  ...   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64  ...   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90  ...   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64  ...   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95  ...   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72  ...   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76  ...   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68  ...   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68  ...   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  正常  低体重  超重  肥胖  \n",
       "0       85  3775     100  163.0  51.299999  19.309999   1    0   0   0  \n",
       "1       66  3683     100  163.0  66.599998  25.070000   0    0   1   0  \n",
       "2       76  3331     100  157.0  60.000000  24.340000   0    0   1   0  \n",
       "3       85  3701     100  160.0  50.700001  19.799999   1    0   0   0  \n",
       "4       70  3592     100  167.0  63.900002  22.910000   0    0   1   0  \n",
       "..     ...   ...     ...    ...        ...        ...  ..  ...  ..  ..  \n",
       "588     78  2255      70  158.0  49.000000  19.629999   1    0   0   0  \n",
       "589     72  2937      85  161.0  55.700001  21.490000   1    0   0   0  \n",
       "590     72  2592      76  165.0  48.599998  17.850000   1    0   0   0  \n",
       "591     78  1829      60  154.0  43.599998  18.379999   1    0   0   0  \n",
       "592     85  2962      85  162.0  55.299999  21.070000   1    0   0   0  \n",
       "\n",
       "[593 rows x 21 columns]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_4(x):\n",
    "    if x>=25.3:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "df_girl['肥胖'] = df_girl['BMI'].apply(convert_4)\n",
    "df_girl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2ca0cfd1970>"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5),dpi=100)\n",
    "boy_bmi = [df_boy['正常'].sum(),df_boy['低体重'].sum(),df_boy['超重'].sum(),df_boy['肥胖'].sum()]\n",
    "girl_bmi = [df_girl['正常'].sum(),df_girl['低体重'].sum(),df_girl['超重'].sum(),df_girl['肥胖'].sum()]\n",
    "labels = ['正常','低体重','超重','肥胖']\n",
    "def func(pct):\n",
    "    return r'%0.1f'%(pct)+ '%'\n",
    "\n",
    "plt.pie(boy_bmi,\n",
    "        autopct=lambda pct:func(pct),\n",
    "        radius=1,\n",
    "        pctdistance=0.85,\n",
    "        wedgeprops=dict(linewidth=3,width=0.4,edgecolor='w'),\n",
    "        labels=labels)\n",
    "plt.figtext(x=0.235,y=0.5,s='男生')\n",
    "plt.pie(girl_bmi,\n",
    "        autopct='%0.1f%%',\n",
    "        radius=0.7,\n",
    "        pctdistance=0.7,\n",
    "        wedgeprops=dict(linewidth=3,width=0.7,edgecolor='w'))\n",
    "plt.title(label='男、女生体重指数(BMI)占比',\n",
    "          color='black',\n",
    "          size=15,\n",
    "          weight='bold')\n",
    "plt.figtext(x=0.435,y=0.5,s='女生')\n",
    "plt.legend(labels,loc = 'upper right',bbox_to_anchor=[0.75,0,0.4,1],title = 'BMI等级')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
